Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at √s = 13.6 TeV

The CMS collaboration

doi:10.1088/1748-0221/20/04/P04021

Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at √s = 13.6 TeV

The CMS collaboration

Yerevan Physics Institute
Yerevan State University
Institut für Hochenergiephysik
Vienna University of Technology
University of Antwerp
Vrjie Universiteit Brussel
Ghent University
Université Libre de Bruxelles
University of Louvain
Centro Brasileiro de Pesquisas Fisicas
Rua São Francisco de Xavier
FACAMP—Faculdades de Campinas
University of Campinas (UNICAMP)
Universidade Federal do Rio Grande do Sul
São Paulo State University
Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences
University of Sofia
Universidad de Tarapacá
Beihang University
Tsinghua University
University of Chinese Academy of Sciences
Institute of High Energy Physics, Chinese Academy of Sciences
China Center of Advanced Science and Technology World Laboratory
China Spallation Neutron Source
State Key Laboratory of Nuclear Physics and Technology
South China Normal University
Sun Yat-Sen University
University of Science and Technology of China
Nanjing Normal University
University of Shanghai for Science and Technology
Fudan University
Zhejiang University
Universidad de Los Andes, Colombia
Universidad de Antioquia
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split
Faculty of Science, University of Split
Ruđer Bošković Institute
European Organization for Nuclear Research
University of Cyprus
Charles University
Escuela Politecnica Nacional
Universidad San Francisco de Quito
Cairo University
Egyptian Network of High Energy Physics
Helwan University
Suez University
Fayoum University
National Institute of Chemical Physics and Biophysics, Tallinn
University of Helsinki
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Purdue University
Ip Paris
Université de Haute Alsace
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Université Paris-Sud
Institut de Physique des 1 Infinis de Lyon (IP2I)
Istinye University
Georgian Technical University
Tbilisi State University
RWTH Aachen University
The University of the State of Amazonas
c/o DESY
Universität Hamburg
Bergische Universität Gesamthochschule Wuppertal
Joint Faculty of the Brandenburg University of Technology Cottbus Senftenberg
Research Centre Julich
Institute of Meteorology and Climate Research
NCSR Demokritos
University of Athens
National Technical University of Athens
University of Ioannina
Wigner Research Centre for Physics
Institute of Nuclear Research ATOMKI
Eötvös Loránd University
Faculty of Science
University of Debrecen
Eszterhazy Karoly University
Panjab University
Punjab Agricultural University
University of Delhi
Indian Institute of Technology Kanpur
Saha Institute of Nuclear Physics
University of Visva-Bharati
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Indian Institute of Science
Amity University
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UPES — University of Petroleum and Energy Studies
IIT Bhubaneswar
An OCC of Homi Bhabha National Institute
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University of Hyderabad
Indian Institute of Science Education and Research (IISER)
Isfahan University of Technology
Sharif University of Technology
Institute for Research in Fundamental Sciences (IPM)
University of Science and Technology of Mazandaran, Behshahr
Arak University
University College Dublin
INFN Sezione di Bari
Università degli studi di Bari Aldo Moro
Politecnico di Bari
INFN Sezione di Bologna
University of Bologna
ENEA Climate Modeling and Impacts
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Università degli Studi di Catania
Istituto Nazionale di Fisica Nucleare, Sezione di Firenze
University of Florence
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Scuola Normale Superiore di Pisa
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University of Siena
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University of Turin
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Kyungpook National University
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Yonsei University
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American University of the Middle East
Kuwait University
Riga Technical University
University of Latvia (LU)
Vilnius University
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University Malaya
Universidad de Sonora
CINVESTAV-IPN
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Universidad Iberoamericana
Benemerita Universidad Autonoma de Puebla
University of Montenegro
University of Canterbury
Quaid-i-Azam University
Agh University of Science and Technology Faculty of Computer Science
National Centre for Nuclear Research
University of Warsaw
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Lisboa
University of Belgrade
University of Belgrade
Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT)
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ETH Zurich
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National Central University
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Izmir Bakircay University
Adiyaman University
Bozok Universitesi Rektörlügü
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Bogazici University
Marmara University
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Hacettepe University
Erzincan University
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Yildiz Technical University
Academy of Science of Ukraine
Kharkov Institute of Physics and Technology
University of Bristol
CCLRC Rutherford Appleton Laboratory
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Imperial College London
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Monash University
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Baylor University
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Boston University
Women and Infants Hospital of Rhode Island-Warren Alpert Medical School of Brown University
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University of California, Riverside
University of California, San Diego
University of California, Santa Barbara
California Institute of Technology
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The United States Naval Academy
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Ain Shams University
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Horia Hulubei National Institute of Physics and Nuclear Engineering
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Institute of Nuclear Physics of the Uzbekistan Academy of Sciences

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Abstract

"Soft"muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously suppressing backgrounds from long-lived light-flavor hadron decays, is therefore important for the success of the CMS physics program. Multivariate techniques have been shown to deliver better muon identification performance than traditional selection techniques. To take full advantage of the large data set currently being collected during Run 3 of the CERN LHC, a new multivariate classifier based on a gradient-boosted decision tree has been developed. It offers a significantly improved separation of signal and background muons compared to a similar classifier used for the analysis of the Run 2 data.

Original language	English
Article number	P04021
Journal	Journal of Instrumentation
Volume	20
Issue number	4
DOIs	https://doi.org/10.1088/1748-0221/20/04/P04021
Publication status	Published - 1 Apr 2025

Keywords

Data processing methods
Performance of High Energy Physics Detectors

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10.1088/1748-0221/20/04/P04021

Cite this

@article{019c884c7b6c4c588ac1e5a59afb3547,

title = "Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at √s = 13.6 TeV",

abstract = "{"}Soft{"}muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously suppressing backgrounds from long-lived light-flavor hadron decays, is therefore important for the success of the CMS physics program. Multivariate techniques have been shown to deliver better muon identification performance than traditional selection techniques. To take full advantage of the large data set currently being collected during Run 3 of the CERN LHC, a new multivariate classifier based on a gradient-boosted decision tree has been developed. It offers a significantly improved separation of signal and background muons compared to a similar classifier used for the analysis of the Run 2 data.",

keywords = "Data processing methods, Performance of High Energy Physics Detectors",

author = "\{The CMS collaboration\} and V. Chekhovsky and A. Hayrapetyan and V. Makarenko and A. Tumasyan and W. Adam and Andrejkovic, \{J. W.\} and L. Benato and T. Bergauer and S. Chatterjee and K. Damanakis and M. Dragicevic and Hussain, \{P. S.\} and M. Jeitler and N. Krammer and A. Li and D. Liko and I. Mikulec and J. Schieck and R. Sch{\"o}fbeck and D. Schwarz and M. Sonawane and W. Waltenberger and Wulz, \{C. E.\} and T. Janssen and H. Kwon and \{Van Laer\}, T. and \{Van Mechelen\}, P. and N. Breugelmans and J. D'Hondt and S. Dansana and \{De Moor\}, A. and M. Delcourt and F. Heyen and Y. Hong and S. Lowette and I. Makarenko and D. M{\"u}ller and S. Tavernier and M. Tytgat and F. Beaudette and G. Boldrini and A. Cappati and O. Davignon and \{De Wit\}, A. and \{Granier de Cassagnac\}, R. and M. Nguyen and C. Ochando and R. Salerno and A. Zabi and A. Zghiche",

note = "Publisher Copyright: {\textcopyright} 2025 CERN for the benefit of the CMS collaboration. Published by IOP Publishing Ltd on behalf of Sissa Medialab.",

year = "2025",

month = apr,

day = "1",

doi = "10.1088/1748-0221/20/04/P04021",

language = "English",

volume = "20",

journal = "Journal of Instrumentation",

issn = "1748-0221",

publisher = "Institute of Physics",

number = "4",

}

TY - JOUR

T1 - Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at √s = 13.6 TeV

AU - The CMS collaboration

AU - Chekhovsky, V.

AU - Hayrapetyan, A.

AU - Makarenko, V.

AU - Tumasyan, A.

AU - Adam, W.

AU - Andrejkovic, J. W.

AU - Benato, L.

AU - Bergauer, T.

AU - Chatterjee, S.

AU - Damanakis, K.

AU - Dragicevic, M.

AU - Hussain, P. S.

AU - Jeitler, M.

AU - Krammer, N.

AU - Li, A.

AU - Liko, D.

AU - Mikulec, I.

AU - Schieck, J.

AU - Schöfbeck, R.

AU - Schwarz, D.

AU - Sonawane, M.

AU - Waltenberger, W.

AU - Wulz, C. E.

AU - Janssen, T.

AU - Kwon, H.

AU - Van Laer, T.

AU - Van Mechelen, P.

AU - Breugelmans, N.

AU - D'Hondt, J.

AU - Dansana, S.

AU - De Moor, A.

AU - Delcourt, M.

AU - Heyen, F.

AU - Hong, Y.

AU - Lowette, S.

AU - Makarenko, I.

AU - Müller, D.

AU - Tavernier, S.

AU - Tytgat, M.

AU - Beaudette, F.

AU - Boldrini, G.

AU - Cappati, A.

AU - Davignon, O.

AU - De Wit, A.

AU - Granier de Cassagnac, R.

AU - Nguyen, M.

AU - Ochando, C.

AU - Salerno, R.

AU - Zabi, A.

AU - Zghiche, A.

PY - 2025/4/1

Y1 - 2025/4/1

N2 - "Soft"muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously suppressing backgrounds from long-lived light-flavor hadron decays, is therefore important for the success of the CMS physics program. Multivariate techniques have been shown to deliver better muon identification performance than traditional selection techniques. To take full advantage of the large data set currently being collected during Run 3 of the CERN LHC, a new multivariate classifier based on a gradient-boosted decision tree has been developed. It offers a significantly improved separation of signal and background muons compared to a similar classifier used for the analysis of the Run 2 data.

AB - "Soft"muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously suppressing backgrounds from long-lived light-flavor hadron decays, is therefore important for the success of the CMS physics program. Multivariate techniques have been shown to deliver better muon identification performance than traditional selection techniques. To take full advantage of the large data set currently being collected during Run 3 of the CERN LHC, a new multivariate classifier based on a gradient-boosted decision tree has been developed. It offers a significantly improved separation of signal and background muons compared to a similar classifier used for the analysis of the Run 2 data.

KW - Data processing methods

KW - Performance of High Energy Physics Detectors

UR - https://www.scopus.com/pages/publications/105004415262

U2 - 10.1088/1748-0221/20/04/P04021

DO - 10.1088/1748-0221/20/04/P04021

M3 - Article

AN - SCOPUS:105004415262

SN - 1748-0221

VL - 20

JO - Journal of Instrumentation

JF - Journal of Instrumentation

IS - 4

M1 - P04021

ER -

Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at √s = 13.6 TeV

Abstract

Keywords

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